Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:H/I:L/A:LCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-22421 is a high-severity Relative Path Traversal (CWE-23) vulnerability in Jupyter Jupyterlab. Its CVSS base score is 7.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data Obfuscation (T1001); ranked in the top 49% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-3 (Access Enforcement) and AC-4 (Information Flow Enforcement) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-0239
Vulnerability Data
JupyterLab is an extensible environment for interactive and reproducible computing, based on the Jupyter Notebook and Architecture. Users of JupyterLab who click on a malicious link may get their `Authorization` and `XSRFToken` tokens exposed to a third party when running…
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an older `jupyter-server` version. JupyterLab versions 4.1.0b2, 4.0.11, and 3.6.7 are patched. No workaround has been identified, however users should ensure to upgrade `jupyter-server` to version 2.7.2 or newer which includes a redirect vulnerability fix.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V5.3.2
Mitigating Controls (NIST 800-53 r5) AI
Access enforcement directly stops unauthorized actors from obtaining sensitive information.
Information flow enforcement structurally prevents sensitive data from reaching unauthorized recipients.
Protection of information at rest prevents unauthorized exposure of stored sensitive data.
Transmission confidentiality mechanisms stop exposure of sensitive data on the wire.
Least privilege reduces the set of actors who can reach sensitive information.
Explicit validation of path inputs stops .. sequences from ever being interpreted by the file system.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly require input validation and path sanitization that prevent relative traversal.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
A.5.10 only requires awareness, a topic-specific acceptable-use policy stating expected behaviours/monitoring, and procedures referencing classification and other controls; per the owner's explicit rule on labelling and similar dependency-style clauses, this does not itself prevent exposure (CWE-200) unless access/DLP/etc. act on it.
Requiring the return of all physical and electronic assets plus secure deletion of organizational data from personal devices reduces the chance that sensitive information remains accessible to departing personnel.
A.5.12 only defines and communicates a classification scheme so that protection needs can be understood; per the owner's explicit rule, labeling/classification does not itself prevent exposure and requires other controls (e.g. access enforcement) to act on it.
By requiring classification labels and metadata on all information assets, the control makes it harder for sensitive data to be inadvertently exposed because downstream systems and users can recognize and enforce handling rules based on those labels.
Hardening callouts derived
DISA STIG baselines are authored against specific weaknesses. Where this CVE cites a broad weakness class, the mapping is not asserted at that abstraction (owner ruling, 2026-09-25: grade only at Base and below), so no rule is shown here rather than a rule shown against the wrong grain.